1486 results about "Metapopulation" patented technology

Methods are provided for reducing the complexity of a population of nucleic acids prior to performing an analysis of the nucleic acids, e.g., sequence analysis. The methods result in a subset of the initial population enriched for a target region, which is typically located within one or more target fragments. The methods are particularly useful for analyzing populations having a high degree of complexity, e.g., chromosomal-derived DNA, whole genomic DNA, or mRNA populations.

Provided in the invention is a task scheduling method based on heredity and ant colony in a cloud computing environment. The method comprises the following methods: S1, initializing population; S2, selecting individuals according to a wheel disc type selection strategy; S3, carrying out crossover operation on the individuals according to crossover probability and carrying out reversion mutation operation according to a mutation probability so as to generate a new colony; S4, updating the new generated colony; S5, determining whether a dynamic fusion condition is met; S6, initializing ant pheromone by using an optimal solution found by heredity; S7, calculating probabilities of moving to next nodes by all ants and moving all the ants to the next nodes according to the probabilities; S8, enabling M ants to travelling N resource nodes and carrying out pheromone updating on an optimal ant cycle; S9, carrying out pheromone updating on all paths; and S10, determining whether an ant end condition is met and outputting an optimal solution. According to the invention, respective advantages of a genetic algorithm and an ant colony algorithm are drawn and respective defects are overcome; and on the basis of dynamic fusion of the two algorithms, time and efficiency of exact solution solving are both considered.

The invention relates to a two-stage hybrid particle swarm optimization clustering method, which is mainly used for solving the problems of greater time consumption and low accuracy of the conventional particle swarm optimization K-mean clustering method when the number of dimensions of samples is higher. The technical scheme disclosed by the invention comprises the following steps: (1) reading a data set and the number K of clusters; (2) taking statistics on information of dimensionality; (3) standardizing the dimensionality; (4) calculating a similarity matrix; (5) generating a candidate initial clustering center; (6) performing particle swarm K-mean partitional clustering; and (7) outputting a particle swarm optimal fitness value and a corresponding data set class cluster partition result. According to the two-stage hybrid particle swarm optimization clustering method disclosed by the invention, the first-stage clustering is firstly performed by adopting agglomerative hierarchical clustering, a simplified particle encoding way is provided, the second-stage clustering is performed on data by particle swarm optimization K-mean clustering, the advantages of hierarchical agglomeration, K-mean and particle swarm optimization methods are integrated, the clustering speed is accelerated, and the global convergence ability and the accuracy of the clustering result of the method are improved.

The invention discloses a dynamic resource allocation method based on evolutionary game in a mobile edge computing system. The method comprises the following steps that (1) a network is divided into a plurality of areas according to the network coverage condition, accessible service points in the areas are different, and a centralized controller is arranged in the network; (2) terminals with the task unloading need in the same area form a population, and the terminals in the population establish task unloading cost functions; (3) all terminals in each population randomly select accessible SPs in an SP selection strategy set; the evolutionary game is established in each population in the network; (4) the terminals in each population compute task unloading costs and send the SP selection strategies and the cost information to the controller; (5) the population carries out SP selection strategy update according to dynamic copy; and (6) the dynamic copy reaches evolution equilibrium. The method fully utilizes the computing resources and the radio resources of the SPs, aims at the equal task unloading costs of all terminals in the populations and meets the task unloading need of each mobile terminal based on the evolutionary game.

The invention discloses a hybrid particle swarm optimization algorithm in combination with a genetic algorithm. The advantages of strong global search capability of a particle swarm optimization (PSO)algorithm and high local convergence speed of the genetic algorithm (GA) are integrated. Firstly, global search is performed by virtue of the characteristic of the strong global search capability ofthe PSO algorithm, and when iteration is performed for a specified algebra and is close to a globally optimal solution, at the moment, a whole population enters a neighborhood of the globally optimalsolution; and secondly, local quick search is performed in the neighborhood of the globally optimal solution by utilizing an improved genetic algorithm, so that the globally optimal solution is reached finally.

The invention discloses a method for partitioning communities in a complex dynamic network by virtue of multi-objective local search based on decomposition, and the method is mainly used for solving the problem of poor community partitioning accuracy in the course of processing the complex dynamic network in the prior art. The method is implemented through the following steps: (1) determining objective functions; (2) constructing an initial solution population, and initializing individuals in the solution population by a neighborhood real-number encoding method; (3) sequentially selecting the individuals from the solution population and then carrying out cross variation on the individuals to obtain progeny individuals; (4) updating the solution population by virtue of the progeny individuals; (5) locally searching and updating the solution population; (6) judging whether the population evolution process is terminated: if iterations reach the preset times, executing a step (7), otherwise, transferring to the step (3); and (7) selecting the optimum community partition according to the maximum module density principle. The method disclosed by the invention has the beneficial effects that two objective functions can be optimized at the same time, synchronous analysis of community partition and community evolution is realized, the community partitioning accuracy is improved, and the problem of detection of a community structure in the complex dynamic network can be solved.

The invention discloses a cascade hydropower station group optimized dispatching method based on an improved quantum-behaved particle swarm algorithm. The problems that local optimum happens to the quantum-behaved particle swarm algorithm at the later iteration period due to premature convergence for the reason that population diversity is decreased, and an obtained hydropower station group dispatching scheme is not the optimal scheme are mainly solved. The hydropower station group optimized dispatching method based on the improved quantum-behaved particle swarm algorithm is characterized by comprising the steps that first, power stations participating in calculation are selected, and the corresponding constraint condition of each power station is set; then, a two-dimensional real number matrix is used for encoding individuals; afterwards, a chaotic initialization population is used for improving the quality of an initial population, the fitness of each particle is calculated through a penalty function method, the individual extreme value and the global extreme value are updated, an update strategy is weighed, the optimum center location of the population is calculated, neighborhood mutation search is conducted on the global optimum individual, the positions of all the individuals in the population are updated according to a formula, and whether a stopping criterion is met or not is judged. The hydropower station group optimized dispatching method based on the improved quantum-behaved particle swarm algorithm is easy to operate, small in number of control parameters, high in convergence rate, high in computation speed, high in robustness, reasonable and effective in result, and applicable to optimized dispatching of cascade hydropower station groups and optimal allocation of water resources.

The invention relates to a power control optimization algorithm in a cognitive radio network, which belongs to the field of system resource allocation. The algorithm comprises the following steps: 1, initializing the iteration number of the algorithm, the positions and speed of particles and the basic parameters of the particle swarm; 2, calculating a fitness function value, setting the position Xa of an individual particle as the initial best position, and setting the particle with the best function value in the swarm as the initial best swarm position Gbestk; 3, searching based on a PSO algorithm, updating the best positions of the particles and the swarm and updating the speed and positions of the particles by using a fundamental formula of the particle swarm; and 4, setting a termination standard. The invention conducts study on the non-convex optimization problem controlled by the cognitive radio power and puts forward an improved particle swarm-based power control algorithm which allows utility functions such as an S-type function a convex function and the like to be non-concave, thereby conforming to the actual network better. Parameter adjustment is performed by the particle swarm algorithm to guarantee the global astringency of the algorithm. The algorithm of the invention has better validity and rapidity.

The invention discloses an arc fault diagnosis method based on time domain feature parameter fusion. The arc fault diagnosis method comprises the steps of: step 1, designing a BP neural network used for arc fault diagnosis, wherein the design is implemented by (1) determining input and output modes of the neural network, (2) acquiring training set and test set samples of the neural network, (3) determining a number of layers of the BP neural network, (4) determining a number of nerve cells of a network hidden layer, and (5) selecting training parameters of the BP neural network; step 2, optimizing the BP neural network by adopting a genetic algorithm in a process shown in the chart 7, wherein the genetic algorithm optimization process is mainly implemented by (1) coding individuals and initializing a population, (2) calculating fitness, (3) and generating a new population. The arc fault diagnosis method has the beneficial effects that: the genetic algorithm optimized BP neural networkhas better performance compared with a BP neural network trained by using random weights and threshold values, and has relatively higher arc fault identification accuracy rate.

The invention relates to a real-time path planning method of AUV (Autonomous Underwater Vehicle), in particular to a method for carrying out online, real-time local path planning according to an online map in an AUV real-time collision preventation process. The method comprises the steps of: setting the quantity of small populations according to the quantity of path points of the AUV, initializing; carrying out immune selection on each small population to obtain subgroups; carrying out genetic manipulation on one subgroup, carrying out cell cloning on the other subgroup; then clustering through a vaccination and an antibody to form the next generation of small population, judging whether the next generation of small population meets the conditions or not; if yes, selecting optimal individuals of the small populations; and selecting the optimal individuals from the set consisting of all optimal individuals to be used as a planning path. According to the invention, the diversity of the population is maintained by using an antibody clustering principle, the premature convergence of an algorithm is avoided, and the global optimization is facilitated. The established immune genetic algorithm is used for clustering and analyzing generated filial generations by adopting a self-regulating mechanism, and the diversity of the population is ensured.

Methods are provided for reducing the complexity of a population of nucleic acids prior to performing an analysis of the nucleic acids, e.g., sequence analysis. The methods result in a subset of the initial population enriched for a target region, which is typically located within one or more target fragments. The methods are particularly useful for analyzing populations having a high degree of complexity, e.g., chromosomal-derived DNA, whole genomic DNA, or mRNA populations.

The invention discloses a sensor target assignment method for a multi-objective optimization differential evolution algorithm. The method includes the steps that objective importance degree calculation is carried out according to objective information, a sensor target assignment constraint multi-objective optimization function is built, distribution scheme codes and initial population chromosomes are generated, offspring scheme populations are generated through the differential evolution algorithm, population combination and screening are carried out, and a distribution scheme Pareto front-end solution set is obtained. The method is combined with the differential evolution algorithm, is easy to use in terms of population difference heuristic random search, is good in robustness and has the advantages of being high in global search ability and the like. A Pareto set multi-objective optimization assignment strategy is provided. A sensor utilization rate function is added on the basis of a sensor target monitoring efficiency function, an assignment problem is converted into a multi-objective optimization problem, sensor resources can be saved as much as possible on the condition that monitoring precision requirements are met, and reasonable and effective assignment of the sensor resources is achieved.

The invention discloses a hyperspectral image wave band selection method based on the quantum-behaved particle swarm optimization algorithm to mainly solve the problems that in the prior art, searching capacity is low and classification accuracy is not high. The hyperspectral image wave band selection method includes the steps of firstly, inputting hyperspectral gray level images, and setting up a training set through samples with labels; secondly, initiating position vectors, code vectors, fitness values and local optimal information of particles and global optimal information of population; thirdly, renewing the position vectors and the code vectors of the particles; fourthly, calculating the fitness values of the particles according to the renewed code vectors; fifthly, renewing the local optimal information of the particles and the global optimal information of the population; sixthly, judging whether iteration is stopped or not, outputting the optimal wave bands corresponding to the global optimal information if the stopping conditions are satisfied, and executing the third step if the stopping conditions are not satisfied. By means of the hyperspectral image wave band selection method, effectiveness of wave band selection is improved, the optimal wave bands can be selected out as less as possible in a self-adaption mode on the premise that classification accuracy is ensured, and the hyperspectral image wave band selection method can be used for preprocessing the hyperspectral images before classification.

Methods are provided for reducing the complexity of a population of nucleic acids prior to performing an analysis of the nucleic acids, e.g., sequence analysis. The methods result in a subset of the initial population enriched for a desired property, or lacking nucleic acids having an undesired property. The methods are particularly useful for analyzing populations having a high degree of complexity, e.g., chromosomal-derived DNA, whole genomic DNA, or mRNA populations. In addition, such methods allow for analysis of pooled samples.

The invention discloses a convolution neural network structure searching method and a system based on an evolutionary algorithm. The method comprises the steps of inputting a data set and setting preset parameters to obtain an initial population. By the controller TC as the main thread popping the initial population into the queue q and opening the queue manager tq and the message manager tm, After the queue manager TQ is turned on, the untrained chromosomes in the queue Q are pop-up and decoded, and a worker manager TW as an independent temporary thread is started to calculate the fitness forthe training, Through the cooperation of controller TC, queue manager TQ, worker manager TW and message manager TM, the parallel search of convolution neural network structure based on evolutionary algorithm is completed and the best model is output. The invention can realize automatic modeling, parameter adjustment and training for a given data set, has the advantages of high performance, large-scale, flow-chart and good certainty, and is particularly suitable for deployment and implementation on a high-performance computer cluster.

The invention relates to an automatic calibration parameter optimization method of an engine based on genetic algorithm. The method comprises the following steps: introducing a genetic algorithm based on the original calibration technology; and establishing the corresponding original population, the fitness evaluation formula, the crossover rate, the mutation rate and the terminal conditions for each parameter by utilizing the genetic algorithm when the engine control parameters are optimized, and carrying out genetic algorithm optimization operation, thus the optimal gene is obtained as the control parameter in an MAP. The method has the beneficial effects that the method does not rely on any subjective factor, and has high objectivity, as long as engineers provide boundary conditions of the calibration objects and the final optimizing target, the optimization configuration of the selected parameters can be carried out automatically through the method; simultaneously the method has very high efficiency, multiple iterations can be carried out in very short time, thus the optimal solution can be obtained rapidly; and the method has high spreadability, and can spread the whole constrained range, thus the accuracy of optimization is improved.

The invention discloses a nonlinear path planning method based on a hybrid pigeon population algorithm and belongs to the technical field of a single unmanned aerial vehicle. The method comprises determining a flight range and obstacles, initializing various parameters, producing a population initial state value through a random interval method, updating a population position and rate through an ego and social learning factor formula which has an inertia matrix and produces cosine transformation along with the inertia matrix according to the ideology of combination of particle swarm optimization algorithm self-learning and social learning in a map and a compass operator, after cycling upper limit, feeding the data into a landmark operator, sorting the pigeon populations according to adaptability values, recording a center position, calculating the iterative population quantity through a damping matrix producing cosine transformation, updating a position and a rate, and outputting the optimal path. The method improves the accuracy of the algorithm, can produce the optimal solution or the second-best solution very close to the optimal solution and can improve stability of the algorithm and the rate of the path planning.

The invention discloses a method for cloud manufacturing resource optimization configuration based on an improved whale algorithm, and the method comprises the steps: building a problem model, and defining a fitness function; setting improved whale algorithm parameters, and generating an initial population; Calculating fitness values of all individuals in the population, obtaining a current optimal resource allocation scheme and converting the current optimal resource allocation scheme into whale individual position vectors; Introducing a parameter p, and judging whether p is less than or equal to 0.5; If not, performing spiral motion iteration updating to complete population updating; If yes, whether the value A (1) of the coefficient vector of the improved whale algorithm is met or not is judged; If yes, performing shrinkage encircling iteration updating; If not, performing random search predation iteration updating; Obtaining a current optimal resource configuration scheme; Adding 1to the number of iterations, and judging whether the current number of iterations is smaller than the maximum number of iterations; If yes, repeating the operation; And if not, outputting the currentoptimal resource configuration scheme. The whale algorithm is improved, so that the algorithm convergence speed is higher, the optimal solution is easier to achieve, and a new method is provided forsolving the problem of resource allocation.

The invention relates to a forecasting method for a multi-stage differential evolution protein structure based on abstract bulge estimation. The method comprises the following steps: firstly, calculating the distance from each conformation individual in a current colony to a new conformation and performing ascending sorting according to the distance; then selecting the part of the new conformation individual close to a abstract bulge lower-limit estimation support surface of the conformation individual, thereby acquiring an energy lower-limit estimation value of the new conformation individual; calculating an average estimation error between the energy lower-limit estimation value of all the new conformation individuals and a practical energy value; dividing the whole algorithm into a plurality of optimizing stages according to the change in the average estimation error; judging the stage of the present iteration according to the average estimation error in the last iteration; and designing different strategies for all the stages and generating the new conformation individual. The forecasting method for the multi-stage differential evolution protein structure based on the colony abstract bulge estimation provided by the invention is high in forecasting precision and low in calculation cost.

The invention discloses an intelligent bus scheduling method based on a hybrid heuristic algorithm. A simulated annealing algorithm and a generic algorithm are combined, and an elitism strategy and a fitness stretching function are added. Individuals with maximum fitness in a population of each generation are directly retained to a next generation, so that the individuals are protected from being damaged by crossover and mutation operation. Through the fitness stretching function, differences among the individuals are reduced at the initial stage of the algorithm, so that the diversity of the population is increased, and the generic algorithm is prevented from falling into a local optimal solution. At the later stage of the algorithm, the differences among the individuals are increased, so that the selection probability of excellent individuals is increased, and a convergence speed is increased. The intelligent bus scheduling method is high in speed; an optimized scheduling plan can be obtained within a short time under the condition of given departure time frequency; and the waiting time of passengers is shortened greatly. The departure frequency can be adjusted dynamically, so that the departure frequency is accordant with the change rule of a total passenger flow volume. The departure time intervals can be adjusted dynamically, so that the waiting time of the passengers is shortened greatly.

The invention provides a multi-objective quantum-behaved particle swarm algorithm-based unmanned aerial vehicle cooperative task distribution method. According to the method, an unmanned aerial vehicle cooperative task distribution model is established; and three indexes such as an objective earlier-stage task execution success probability, an unmanned aerial vehicle survival probability and a task completion time in an unmanned aerial vehicle cooperation task distribution problem respectively serve as optimized objective functions of a multi-objective task distribution problem, so that the simultaneous optimization of the three indexes is realized. Aiming at the characteristics of the unmanned aerial vehicle cooperation task distribution problem, a repairing operator aiming at the earlier task constraint and a repairing operator aiming at the in-task constraint are designed, so that the quality of solution in populations is improved; and a new population variation mechanism, so that the convergence speed of the algorithm is effectively improved.

The invention relates to a breeding method and a breeding device for insects and specifically relates to a method and a device for indoor breeding of phauda flammans walker. The method comprises the following steps: breeding larva; breeding pupas; breeding adults and spawns. Specifically, the steps are as follows: breeding the early hatched larva of the phauda flammans walker in a larva breeding device, and taking ficus microcarpa leaves as foodstuff till pupating; after identifying male and female pupas, separately putting into square plastic boxes, and pairing and putting into an adult mating device after eclosion; putting the mated adults into an adult oviposition device and waiting for oviposition; collecting the filtering paper with the eggs generated in the adult oviposition device, putting into a clean square plastic box with a moisturizing filter paper at the bottom, putting into a manual climatic box, and moisturizing everyday till hatching; after hatching the larva, circularly subculture breeding the larva according to the steps. The invention relates to the study on the influences of biological and non-biological factors on the survival and propagation of the phauda flammans walker stocks and provides an insect source with a consistent insect state developing progress.

The invention provides a large-scale intelligent group independent collaborative modeling method for imitation of starling group flight. The method comprises the steps of initializing a population andparameters thereof, setting as follows: in the initial state, all Agents use initial positions as optimal positions, and all Agents use optimal values of six or seven most closest neighbors as localoptimal values; calculating a fitness function, and setting distribution anisotropy of neighbors around each Agent, wherein six or seven most closest neighbors are isotropic, and interaction between the Agents depends on a topological distance; selecting an updating company, comprising the step of selecting one of the six or seven most closest neighbors as an updating company for a certain Agent;defining an interaction relationship of Agents; and updating Agent speed and position. The method has a broad application prospect in military, emergency, industrial and medical care fields like dronecluster intensive formation, group emergency evacuation in a large collection place, large-scale robot group collaborative operation, and disease spread control.

The multi-parking-lot and multi-vehicle-type vehicle path scheduling control method comprises the steps of 1, establishing an objective function by taking the lowest total cost of all delivery vehicles as an objective; Step 2, performing a coding step; 3, performing population initialization; 4, evaluating all the individuals by adopting the objective function as a fitness function; Step 5, performing selection and crossover operation; step 6, performing mutation operation; 7, performing neighborhood search on each individual in the population by using an improved extreme value optimization algorithm; Step 8, calculating fitness of all individuals in the population; Step 9, performing selecting; Step 10, performing elite retention; Step 11, completing iteration in sequence; Step 12, judging whether a termination condition is met or not, the termination condition being that the number of iterations g reaches the maximum number of iterations MaxGen or the number of iterations Nu of whichthe Gb fitness value remains unchanged reaches the specified number of iterations Kbest, if yes, continuing to execute the step 13, and if not, returning to execute the step 5; Step 13, outputting the individual Gb and the fitness value fGb thereof; And 14, interpreting the optimal individual Gb and the fitness value fGb thereof. The invention aims to improve the search efficiency and convergencespeed of the algorithm.